Overview

Dataset statistics

Number of variables20
Number of observations8616
Missing cells19500
Missing cells (%)11.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory160.0 B

Variable types

Numeric1
Text8
Categorical10
Unsupported1

Alerts

NIVEL has constant value ""Constant
Unnamed: 0 is highly overall correlated with DEPARTAMENTO and 2 other fieldsHigh correlation
DEPARTAMENTO is highly overall correlated with Unnamed: 0 and 2 other fieldsHigh correlation
JORNADA is highly overall correlated with PLANHigh correlation
PLAN is highly overall correlated with JORNADAHigh correlation
DEPARTAMENTAL is highly overall correlated with Unnamed: 0 and 2 other fieldsHigh correlation
ZONA is highly overall correlated with Unnamed: 0 and 2 other fieldsHigh correlation
SECTOR is highly imbalanced (62.5%)Imbalance
AREA is highly imbalanced (56.9%)Imbalance
STATUS is highly imbalanced (52.9%)Imbalance
MODALIDAD is highly imbalanced (80.8%)Imbalance
PLAN is highly imbalanced (55.9%)Imbalance
DISTRITO has 206 (2.4%) missing valuesMissing
TELEFONO has 1566 (18.2%) missing valuesMissing
SUPERVISOR has 207 (2.4%) missing valuesMissing
DIRECTOR has 1768 (20.5%) missing valuesMissing
CODIGO has 8616 (100.0%) missing valuesMissing
ZONA has 7080 (82.2%) missing valuesMissing
Unnamed: 0 has unique valuesUnique
CODIGO has unique valuesUnique
CODIGO is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-08-11 04:48:19.573003
Analysis finished2023-08-11 04:48:24.459804
Duration4.89 seconds
Software versionydata-profiling vv4.3.2
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct8616
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4332.8214
Minimum0
Maximum9022
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size67.4 KiB
2023-08-10T22:48:24.722915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile432.75
Q12157.75
median4319.5
Q36487.25
95-th percentile8222.25
Maximum9022
Range9022
Interquartile range (IQR)4329.5

Descriptive statistics

Standard deviation2517.4847
Coefficient of variation (CV)0.58102664
Kurtosis-1.1672341
Mean4332.8214
Median Absolute Deviation (MAD)2165
Skewness0.027487503
Sum37331589
Variance6337729
MonotonicityStrictly increasing
2023-08-10T22:48:24.922432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
5760 1
 
< 0.1%
5774 1
 
< 0.1%
5773 1
 
< 0.1%
5772 1
 
< 0.1%
5771 1
 
< 0.1%
5770 1
 
< 0.1%
5769 1
 
< 0.1%
5768 1
 
< 0.1%
5767 1
 
< 0.1%
Other values (8606) 8606
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
9022 1
< 0.1%
9021 1
< 0.1%
9020 1
< 0.1%
9012 1
< 0.1%
9011 1
< 0.1%
9010 1
< 0.1%
9009 1
< 0.1%
9008 1
< 0.1%
9007 1
< 0.1%
9006 1
< 0.1%

CODIGO
Text

UNIQUE 

Distinct8616
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size67.4 KiB
2023-08-10T22:48:25.378505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters112008
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8616 ?
Unique (%)100.0%

Sample

1st row16-01-0138-46
2nd row16-01-0139-46
3rd row16-01-0140-46
4th row16-01-0141-46
5th row16-01-0142-46
ValueCountFrequency (%)
16-01-0138-46 1
 
< 0.1%
16-01-0565-46 1
 
< 0.1%
16-01-0143-46 1
 
< 0.1%
16-01-0145-46 1
 
< 0.1%
16-01-0147-46 1
 
< 0.1%
16-01-0150-46 1
 
< 0.1%
16-01-0155-46 1
 
< 0.1%
16-01-0428-46 1
 
< 0.1%
16-01-0471-46 1
 
< 0.1%
16-01-0478-46 1
 
< 0.1%
Other values (8606) 8606
99.9%
2023-08-10T22:48:25.970973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 25848
23.1%
0 24108
21.5%
1 13841
12.4%
4 12304
11.0%
6 11683
10.4%
2 5962
 
5.3%
3 4395
 
3.9%
5 3816
 
3.4%
8 3459
 
3.1%
7 3370
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 86160
76.9%
Dash Punctuation 25848
 
23.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24108
28.0%
1 13841
16.1%
4 12304
14.3%
6 11683
13.6%
2 5962
 
6.9%
3 4395
 
5.1%
5 3816
 
4.4%
8 3459
 
4.0%
7 3370
 
3.9%
9 3222
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 25848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 112008
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 25848
23.1%
0 24108
21.5%
1 13841
12.4%
4 12304
11.0%
6 11683
10.4%
2 5962
 
5.3%
3 4395
 
3.9%
5 3816
 
3.4%
8 3459
 
3.1%
7 3370
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 112008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 25848
23.1%
0 24108
21.5%
1 13841
12.4%
4 12304
11.0%
6 11683
10.4%
2 5962
 
5.3%
3 4395
 
3.9%
5 3816
 
3.4%
8 3459
 
3.1%
7 3370
 
3.0%

DISTRITO
Text

MISSING 

Distinct642
Distinct (%)7.6%
Missing206
Missing (%)2.4%
Memory size67.4 KiB
2023-08-10T22:48:26.476321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9882283
Min length3

Characters and Unicode

Total characters50361
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)1.0%

Sample

1st row16-031
2nd row16-031
3rd row16-031
4th row16-005
5th row16-005
ValueCountFrequency (%)
01-403 242
 
2.9%
11-017 175
 
2.1%
05-033 159
 
1.9%
01-411 150
 
1.8%
18-008 128
 
1.5%
01-409 102
 
1.2%
05-007 100
 
1.2%
18-039 98
 
1.2%
13-004 92
 
1.1%
03-002 91
 
1.1%
Other values (632) 7073
84.1%
2023-08-10T22:48:27.016037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14552
28.9%
1 10175
20.2%
- 8410
16.7%
2 3780
 
7.5%
3 3249
 
6.5%
4 2513
 
5.0%
6 1842
 
3.7%
5 1621
 
3.2%
9 1513
 
3.0%
7 1490
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41951
83.3%
Dash Punctuation 8410
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14552
34.7%
1 10175
24.3%
2 3780
 
9.0%
3 3249
 
7.7%
4 2513
 
6.0%
6 1842
 
4.4%
5 1621
 
3.9%
9 1513
 
3.6%
7 1490
 
3.6%
8 1216
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 8410
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50361
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14552
28.9%
1 10175
20.2%
- 8410
16.7%
2 3780
 
7.5%
3 3249
 
6.5%
4 2513
 
5.0%
6 1842
 
3.7%
5 1621
 
3.2%
9 1513
 
3.0%
7 1490
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50361
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14552
28.9%
1 10175
20.2%
- 8410
16.7%
2 3780
 
7.5%
3 3249
 
6.5%
4 2513
 
5.0%
6 1842
 
3.7%
5 1621
 
3.2%
9 1513
 
3.0%
7 1490
 
3.0%

DEPARTAMENTO
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size67.4 KiB
GUATEMALA
2970 
ESCUINTLA
599 
HUEHUETENANGO
495 
QUETZALTENANGO
476 
PETEN
379 
Other values (15)
3697 

Length

Max length14
Median length13
Mean length9.7129759
Min length5

Characters and Unicode

Total characters83687
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowALTA VERAPAZ
2nd rowALTA VERAPAZ
3rd rowALTA VERAPAZ
4th rowALTA VERAPAZ
5th rowALTA VERAPAZ

Common Values

ValueCountFrequency (%)
GUATEMALA 2970
34.5%
ESCUINTLA 599
 
7.0%
HUEHUETENANGO 495
 
5.7%
QUETZALTENANGO 476
 
5.5%
PETEN 379
 
4.4%
SUCHITEPEQUEZ 377
 
4.4%
IZABAL 360
 
4.2%
CHIMALTENANGO 349
 
4.1%
ALTA VERAPAZ 348
 
4.0%
SAN MARCOS 332
 
3.9%
Other values (10) 1931
22.4%

Length

2023-08-10T22:48:27.175158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
guatemala 2970
31.1%
escuintla 599
 
6.3%
huehuetenango 495
 
5.2%
quetzaltenango 476
 
5.0%
verapaz 468
 
4.9%
peten 379
 
4.0%
suchitepequez 377
 
4.0%
izabal 360
 
3.8%
chimaltenango 349
 
3.7%
alta 348
 
3.6%
Other values (13) 2717
28.5%

Most occurring characters

ValueCountFrequency (%)
A 17565
21.0%
E 10750
12.8%
U 7627
9.1%
T 7590
9.1%
L 6167
 
7.4%
G 4412
 
5.3%
N 4130
 
4.9%
M 3823
 
4.6%
I 2681
 
3.2%
C 2566
 
3.1%
Other values (11) 16376
19.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 82765
98.9%
Space Separator 922
 
1.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 17565
21.2%
E 10750
13.0%
U 7627
9.2%
T 7590
9.2%
L 6167
 
7.5%
G 4412
 
5.3%
N 4130
 
5.0%
M 3823
 
4.6%
I 2681
 
3.2%
C 2566
 
3.1%
Other values (10) 15454
18.7%
Space Separator
ValueCountFrequency (%)
922
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 82765
98.9%
Common 922
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 17565
21.2%
E 10750
13.0%
U 7627
9.2%
T 7590
9.2%
L 6167
 
7.5%
G 4412
 
5.3%
N 4130
 
5.0%
M 3823
 
4.6%
I 2681
 
3.2%
C 2566
 
3.1%
Other values (10) 15454
18.7%
Common
ValueCountFrequency (%)
922
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 17565
21.0%
E 10750
12.8%
U 7627
9.1%
T 7590
9.1%
L 6167
 
7.4%
G 4412
 
5.3%
N 4130
 
4.9%
M 3823
 
4.6%
I 2681
 
3.2%
C 2566
 
3.1%
Other values (11) 16376
19.6%

MUNICIPIO
Text

Distinct299
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size67.4 KiB
2023-08-10T22:48:27.431541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length28
Median length24
Mean length12.228877
Min length4

Characters and Unicode

Total characters105364
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.2%

Sample

1st rowCOBAN
2nd rowCOBAN
3rd rowCOBAN
4th rowCOBAN
5th rowCOBAN
ValueCountFrequency (%)
ciudad 1565
 
10.4%
capital 1536
 
10.2%
san 1357
 
9.0%
villa 455
 
3.0%
mixco 420
 
2.8%
nueva 400
 
2.7%
santa 354
 
2.4%
la 265
 
1.8%
quetzaltenango 241
 
1.6%
miguel 193
 
1.3%
Other values (305) 8227
54.8%
2023-08-10T22:48:27.833217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 19529
18.5%
I 7623
 
7.2%
C 7333
 
7.0%
N 7066
 
6.7%
T 6947
 
6.6%
L 6788
 
6.4%
E 6492
 
6.2%
U 6488
 
6.2%
6397
 
6.1%
O 4743
 
4.5%
Other values (15) 25958
24.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 98967
93.9%
Space Separator 6397
 
6.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 19529
19.7%
I 7623
 
7.7%
C 7333
 
7.4%
N 7066
 
7.1%
T 6947
 
7.0%
L 6788
 
6.9%
E 6492
 
6.6%
U 6488
 
6.6%
O 4743
 
4.8%
S 4587
 
4.6%
Other values (14) 21371
21.6%
Space Separator
ValueCountFrequency (%)
6397
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 98967
93.9%
Common 6397
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 19529
19.7%
I 7623
 
7.7%
C 7333
 
7.4%
N 7066
 
7.1%
T 6947
 
7.0%
L 6788
 
6.9%
E 6492
 
6.6%
U 6488
 
6.6%
O 4743
 
4.8%
S 4587
 
4.6%
Other values (14) 21371
21.6%
Common
ValueCountFrequency (%)
6397
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 105364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 19529
18.5%
I 7623
 
7.2%
C 7333
 
7.0%
N 7066
 
6.7%
T 6947
 
6.6%
L 6788
 
6.4%
E 6492
 
6.2%
U 6488
 
6.2%
6397
 
6.1%
O 4743
 
4.5%
Other values (15) 25958
24.6%

ESTABLECIMIENTO
Text

Distinct4609
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Memory size67.4 KiB
2023-08-10T22:48:28.097083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length125
Median length103
Mean length40.211931
Min length3

Characters and Unicode

Total characters346466
Distinct characters49
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2963 ?
Unique (%)34.4%

Sample

1st rowCOLEGIO COBAN
2nd rowCOLEGIO PARTICULAR MIXTO VERAPAZ
3rd rowCOLEGIO "LA INMACULADA"
4th rowESCUELA NACIONAL DE CIENCIAS COMERCIALES
5th rowINSTITUTO NORMAL MIXTO DEL NORTE 'EMILIO ROSALES PONCE'
ValueCountFrequency (%)
de 3500
 
7.7%
colegio 3326
 
7.3%
mixto 2619
 
5.8%
instituto 2437
 
5.4%
liceo 1610
 
3.5%
educacion 1364
 
3.0%
privado 1333
 
2.9%
centro 1127
 
2.5%
diversificada 772
 
1.7%
educativo 742
 
1.6%
Other values (2973) 26568
58.5%
2023-08-10T22:48:28.492057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36806
10.6%
I 36497
10.5%
O 34258
9.9%
E 30600
 
8.8%
A 29397
 
8.5%
C 24226
 
7.0%
T 21605
 
6.2%
N 19971
 
5.8%
L 16256
 
4.7%
R 15742
 
4.5%
Other values (39) 81108
23.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 303421
87.6%
Space Separator 36806
 
10.6%
Other Punctuation 4714
 
1.4%
Dash Punctuation 769
 
0.2%
Decimal Number 356
 
0.1%
Open Punctuation 199
 
0.1%
Close Punctuation 198
 
0.1%
Modifier Symbol 3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 36497
12.0%
O 34258
11.3%
E 30600
10.1%
A 29397
9.7%
C 24226
 
8.0%
T 21605
 
7.1%
N 19971
 
6.6%
L 16256
 
5.4%
R 15742
 
5.2%
D 13007
 
4.3%
Other values (16) 61862
20.4%
Decimal Number
ValueCountFrequency (%)
2 125
35.1%
0 71
19.9%
1 57
16.0%
3 34
 
9.6%
4 20
 
5.6%
7 17
 
4.8%
6 11
 
3.1%
9 7
 
2.0%
8 7
 
2.0%
5 7
 
2.0%
Other Punctuation
ValueCountFrequency (%)
" 2913
61.8%
' 893
 
18.9%
. 783
 
16.6%
, 106
 
2.2%
& 9
 
0.2%
/ 7
 
0.1%
% 2
 
< 0.1%
# 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
36806
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 769
100.0%
Open Punctuation
ValueCountFrequency (%)
( 199
100.0%
Close Punctuation
ValueCountFrequency (%)
) 198
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 303421
87.6%
Common 43045
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 36497
12.0%
O 34258
11.3%
E 30600
10.1%
A 29397
9.7%
C 24226
 
8.0%
T 21605
 
7.1%
N 19971
 
6.6%
L 16256
 
5.4%
R 15742
 
5.2%
D 13007
 
4.3%
Other values (16) 61862
20.4%
Common
ValueCountFrequency (%)
36806
85.5%
" 2913
 
6.8%
' 893
 
2.1%
. 783
 
1.8%
- 769
 
1.8%
( 199
 
0.5%
) 198
 
0.5%
2 125
 
0.3%
, 106
 
0.2%
0 71
 
0.2%
Other values (13) 182
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 346466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36806
10.6%
I 36497
10.5%
O 34258
9.9%
E 30600
 
8.8%
A 29397
 
8.5%
C 24226
 
7.0%
T 21605
 
6.2%
N 19971
 
5.8%
L 16256
 
4.7%
R 15742
 
4.5%
Other values (39) 81108
23.4%

DIRECCION
Text

Distinct5522
Distinct (%)64.5%
Missing57
Missing (%)0.7%
Memory size67.4 KiB
2023-08-10T22:48:28.698519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length110
Median length91
Mean length28.49188
Min length4

Characters and Unicode

Total characters243862
Distinct characters49
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4095 ?
Unique (%)47.8%

Sample

1st rowKM.2 SALIDA A SAN JUAN CHAMELCO ZONA 8
2nd rowKM 209.5 ENTRADA A LA CIUDAD
3rd row7A. AVENIDA 11-109 ZONA 6
4th row2A CALLE 11-10 ZONA 2
5th row3A AVE 6-23 ZONA 11
ValueCountFrequency (%)
zona 3808
 
8.3%
calle 2729
 
6.0%
avenida 2085
 
4.6%
1 1686
 
3.7%
barrio 1052
 
2.3%
colonia 1029
 
2.2%
aldea 988
 
2.2%
el 877
 
1.9%
san 820
 
1.8%
2 610
 
1.3%
Other values (3473) 30096
65.7%
2023-08-10T22:48:29.041454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37221
15.3%
A 35423
14.5%
E 15615
 
6.4%
L 15241
 
6.2%
N 14573
 
6.0%
O 14533
 
6.0%
I 11137
 
4.6%
C 9645
 
4.0%
R 8804
 
3.6%
1 6600
 
2.7%
Other values (39) 75070
30.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 165000
67.7%
Space Separator 37221
 
15.3%
Decimal Number 28893
 
11.8%
Other Punctuation 7874
 
3.2%
Dash Punctuation 4820
 
2.0%
Lowercase Letter 22
 
< 0.1%
Open Punctuation 16
 
< 0.1%
Close Punctuation 16
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 35423
21.5%
E 15615
9.5%
L 15241
9.2%
N 14573
8.8%
O 14533
8.8%
I 11137
 
6.7%
C 9645
 
5.8%
R 8804
 
5.3%
D 5942
 
3.6%
T 5398
 
3.3%
Other values (16) 28689
17.4%
Decimal Number
ValueCountFrequency (%)
1 6600
22.8%
2 4025
13.9%
3 3470
12.0%
4 2945
10.2%
5 2718
9.4%
0 2365
 
8.2%
6 2078
 
7.2%
7 1770
 
6.1%
8 1463
 
5.1%
9 1459
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 4799
60.9%
, 2458
31.2%
" 469
 
6.0%
' 92
 
1.2%
/ 36
 
0.5%
# 19
 
0.2%
; 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
a 20
90.9%
o 2
 
9.1%
Space Separator
ValueCountFrequency (%)
37221
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4820
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 165022
67.7%
Common 78840
32.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 35423
21.5%
E 15615
9.5%
L 15241
9.2%
N 14573
8.8%
O 14533
8.8%
I 11137
 
6.7%
C 9645
 
5.8%
R 8804
 
5.3%
D 5942
 
3.6%
T 5398
 
3.3%
Other values (18) 28711
17.4%
Common
ValueCountFrequency (%)
37221
47.2%
1 6600
 
8.4%
- 4820
 
6.1%
. 4799
 
6.1%
2 4025
 
5.1%
3 3470
 
4.4%
4 2945
 
3.7%
5 2718
 
3.4%
, 2458
 
3.1%
0 2365
 
3.0%
Other values (11) 7419
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 243862
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37221
15.3%
A 35423
14.5%
E 15615
 
6.4%
L 15241
 
6.2%
N 14573
 
6.0%
O 14533
 
6.0%
I 11137
 
4.6%
C 9645
 
4.0%
R 8804
 
3.6%
1 6600
 
2.7%
Other values (39) 75070
30.8%

TELEFONO
Text

MISSING 

Distinct4322
Distinct (%)61.3%
Missing1566
Missing (%)18.2%
Memory size67.4 KiB
2023-08-10T22:48:29.425502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters56400
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2927 ?
Unique (%)41.5%

Sample

1st row77945104
2nd row77367402
3rd row78232301
4th row79514215
5th row79521468
ValueCountFrequency (%)
22067425 21
 
0.3%
79480009 14
 
0.2%
22093200 12
 
0.2%
45353648 11
 
0.2%
77746400 11
 
0.2%
59304894 11
 
0.2%
78899679 10
 
0.1%
22322912 10
 
0.1%
24637777 10
 
0.1%
78394519 9
 
0.1%
Other values (4314) 6934
98.3%
2023-08-10T22:48:29.986841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 7296
12.9%
7 6780
12.0%
4 6339
11.2%
5 5957
10.6%
3 5759
10.2%
0 5087
9.0%
8 5045
8.9%
6 4981
8.8%
1 4583
8.1%
9 4535
8.0%
Other values (6) 38
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 56362
99.9%
Dash Punctuation 20
 
< 0.1%
Other Punctuation 9
 
< 0.1%
Space Separator 7
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 7296
12.9%
7 6780
12.0%
4 6339
11.2%
5 5957
10.6%
3 5759
10.2%
0 5087
9.0%
8 5045
9.0%
6 4981
8.8%
1 4583
8.1%
9 4535
8.0%
Other Punctuation
ValueCountFrequency (%)
, 8
88.9%
/ 1
 
11.1%
Uppercase Letter
ValueCountFrequency (%)
Y 1
50.0%
E 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 56398
> 99.9%
Latin 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 7296
12.9%
7 6780
12.0%
4 6339
11.2%
5 5957
10.6%
3 5759
10.2%
0 5087
9.0%
8 5045
8.9%
6 4981
8.8%
1 4583
8.1%
9 4535
8.0%
Other values (4) 36
 
0.1%
Latin
ValueCountFrequency (%)
Y 1
50.0%
E 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 7296
12.9%
7 6780
12.0%
4 6339
11.2%
5 5957
10.6%
3 5759
10.2%
0 5087
9.0%
8 5045
8.9%
6 4981
8.8%
1 4583
8.1%
9 4535
8.0%
Other values (6) 38
 
0.1%

SUPERVISOR
Text

MISSING 

Distinct608
Distinct (%)7.2%
Missing207
Missing (%)2.4%
Memory size67.4 KiB
2023-08-10T22:48:30.277645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length63
Median length44
Mean length29.097039
Min length14

Characters and Unicode

Total characters244677
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)0.8%

Sample

1st rowMERCEDES JOSEFINA TORRES GALVEZ
2nd rowMERCEDES JOSEFINA TORRES GALVEZ
3rd rowMERCEDES JOSEFINA TORRES GALVEZ
4th rowRUDY ADOLFO TOT OCH
5th rowRUDY ADOLFO TOT OCH
ValueCountFrequency (%)
de 1959
 
5.4%
lopez 592
 
1.6%
martinez 572
 
1.6%
leon 543
 
1.5%
gonzalez 488
 
1.4%
juan 457
 
1.3%
carlos 396
 
1.1%
morales 389
 
1.1%
hernandez 356
 
1.0%
humberto 327
 
0.9%
Other values (1094) 30050
83.2%
2023-08-10T22:48:30.634502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 30691
12.5%
27720
11.3%
E 23149
 
9.5%
R 19345
 
7.9%
O 18763
 
7.7%
I 16333
 
6.7%
L 15441
 
6.3%
N 14367
 
5.9%
S 10000
 
4.1%
D 8202
 
3.4%
Other values (19) 60666
24.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 216827
88.6%
Space Separator 27720
 
11.3%
Dash Punctuation 124
 
0.1%
Other Punctuation 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 30691
14.2%
E 23149
10.7%
R 19345
 
8.9%
O 18763
 
8.7%
I 16333
 
7.5%
L 15441
 
7.1%
N 14367
 
6.6%
S 10000
 
4.6%
D 8202
 
3.8%
C 8033
 
3.7%
Other values (16) 52503
24.2%
Space Separator
ValueCountFrequency (%)
27720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 124
100.0%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 216827
88.6%
Common 27850
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 30691
14.2%
E 23149
10.7%
R 19345
 
8.9%
O 18763
 
8.7%
I 16333
 
7.5%
L 15441
 
7.1%
N 14367
 
6.6%
S 10000
 
4.6%
D 8202
 
3.8%
C 8033
 
3.7%
Other values (16) 52503
24.2%
Common
ValueCountFrequency (%)
27720
99.5%
- 124
 
0.4%
. 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 244677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 30691
12.5%
27720
11.3%
E 23149
 
9.5%
R 19345
 
7.9%
O 18763
 
7.7%
I 16333
 
6.7%
L 15441
 
6.3%
N 14367
 
5.9%
S 10000
 
4.1%
D 8202
 
3.4%
Other values (19) 60666
24.8%

DIRECTOR
Text

MISSING 

Distinct4272
Distinct (%)62.4%
Missing1768
Missing (%)20.5%
Memory size67.4 KiB
2023-08-10T22:48:30.920788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length57
Median length48
Mean length28.673481
Min length1

Characters and Unicode

Total characters196356
Distinct characters34
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2914 ?
Unique (%)42.6%

Sample

1st rowJULIO CESAR VILLELA AMADO
2nd rowVIRGINA SOLANO SERRANO
3rd rowHECOTR WALDEMAR TOT COY
4th rowLUIS FERNANDO SOTO
5th rowMERCEDES QUIROS QUIROS
ValueCountFrequency (%)
de 1329
 
4.6%
lopez 557
 
1.9%
garcia 343
 
1.2%
maria 332
 
1.1%
hernandez 321
 
1.1%
morales 284
 
1.0%
perez 269
 
0.9%
gonzalez 229
 
0.8%
jose 203
 
0.7%
ramirez 199
 
0.7%
Other values (3597) 24838
85.9%
2023-08-10T22:48:31.351134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 25943
13.2%
22056
11.2%
E 18879
 
9.6%
R 15587
 
7.9%
O 14215
 
7.2%
I 13285
 
6.8%
L 12166
 
6.2%
N 11437
 
5.8%
S 8040
 
4.1%
D 7237
 
3.7%
Other values (24) 47511
24.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 174149
88.7%
Space Separator 22056
 
11.2%
Other Punctuation 77
 
< 0.1%
Dash Punctuation 71
 
< 0.1%
Math Symbol 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 25943
14.9%
E 18879
10.8%
R 15587
 
9.0%
O 14215
 
8.2%
I 13285
 
7.6%
L 12166
 
7.0%
N 11437
 
6.6%
S 8040
 
4.6%
D 7237
 
4.2%
C 6214
 
3.6%
Other values (16) 41146
23.6%
Other Punctuation
ValueCountFrequency (%)
. 70
90.9%
, 5
 
6.5%
" 2
 
2.6%
Space Separator
ValueCountFrequency (%)
22056
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 71
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 174149
88.7%
Common 22207
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 25943
14.9%
E 18879
10.8%
R 15587
 
9.0%
O 14215
 
8.2%
I 13285
 
7.6%
L 12166
 
7.0%
N 11437
 
6.6%
S 8040
 
4.6%
D 7237
 
4.2%
C 6214
 
3.6%
Other values (16) 41146
23.6%
Common
ValueCountFrequency (%)
22056
99.3%
- 71
 
0.3%
. 70
 
0.3%
, 5
 
< 0.1%
" 2
 
< 0.1%
+ 1
 
< 0.1%
( 1
 
< 0.1%
) 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 196356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 25943
13.2%
22056
11.2%
E 18879
 
9.6%
R 15587
 
7.9%
O 14215
 
7.2%
I 13285
 
6.8%
L 12166
 
6.2%
N 11437
 
5.8%
S 8040
 
4.1%
D 7237
 
3.7%
Other values (24) 47511
24.2%

NIVEL
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.4 KiB
DIVERSIFICADO
8616 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters112008
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDIVERSIFICADO
2nd rowDIVERSIFICADO
3rd rowDIVERSIFICADO
4th rowDIVERSIFICADO
5th rowDIVERSIFICADO

Common Values

ValueCountFrequency (%)
DIVERSIFICADO 8616
100.0%

Length

2023-08-10T22:48:31.487380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-10T22:48:31.584102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
diversificado 8616
100.0%

Most occurring characters

ValueCountFrequency (%)
I 25848
23.1%
D 17232
15.4%
V 8616
 
7.7%
E 8616
 
7.7%
R 8616
 
7.7%
S 8616
 
7.7%
F 8616
 
7.7%
C 8616
 
7.7%
A 8616
 
7.7%
O 8616
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 112008
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 25848
23.1%
D 17232
15.4%
V 8616
 
7.7%
E 8616
 
7.7%
R 8616
 
7.7%
S 8616
 
7.7%
F 8616
 
7.7%
C 8616
 
7.7%
A 8616
 
7.7%
O 8616
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 112008
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 25848
23.1%
D 17232
15.4%
V 8616
 
7.7%
E 8616
 
7.7%
R 8616
 
7.7%
S 8616
 
7.7%
F 8616
 
7.7%
C 8616
 
7.7%
A 8616
 
7.7%
O 8616
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 112008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 25848
23.1%
D 17232
15.4%
V 8616
 
7.7%
E 8616
 
7.7%
R 8616
 
7.7%
S 8616
 
7.7%
F 8616
 
7.7%
C 8616
 
7.7%
A 8616
 
7.7%
O 8616
 
7.7%

SECTOR
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.4 KiB
PRIVADO
7383 
OFICIAL
899 
COOPERATIVA
 
200
MUNICIPAL
 
134

Length

Max length11
Median length7
Mean length7.1239554
Min length7

Characters and Unicode

Total characters61380
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRIVADO
2nd rowPRIVADO
3rd rowPRIVADO
4th rowOFICIAL
5th rowOFICIAL

Common Values

ValueCountFrequency (%)
PRIVADO 7383
85.7%
OFICIAL 899
 
10.4%
COOPERATIVA 200
 
2.3%
MUNICIPAL 134
 
1.6%

Length

2023-08-10T22:48:31.672169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-10T22:48:31.801056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
privado 7383
85.7%
oficial 899
 
10.4%
cooperativa 200
 
2.3%
municipal 134
 
1.6%

Most occurring characters

ValueCountFrequency (%)
I 9649
15.7%
A 8816
14.4%
O 8682
14.1%
P 7717
12.6%
R 7583
12.4%
V 7583
12.4%
D 7383
12.0%
C 1233
 
2.0%
L 1033
 
1.7%
F 899
 
1.5%
Other values (5) 802
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 61380
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 9649
15.7%
A 8816
14.4%
O 8682
14.1%
P 7717
12.6%
R 7583
12.4%
V 7583
12.4%
D 7383
12.0%
C 1233
 
2.0%
L 1033
 
1.7%
F 899
 
1.5%
Other values (5) 802
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 61380
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 9649
15.7%
A 8816
14.4%
O 8682
14.1%
P 7717
12.6%
R 7583
12.4%
V 7583
12.4%
D 7383
12.0%
C 1233
 
2.0%
L 1033
 
1.7%
F 899
 
1.5%
Other values (5) 802
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 9649
15.7%
A 8816
14.4%
O 8682
14.1%
P 7717
12.6%
R 7583
12.4%
V 7583
12.4%
D 7383
12.0%
C 1233
 
2.0%
L 1033
 
1.7%
F 899
 
1.5%
Other values (5) 802
 
1.3%

AREA
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.4 KiB
URBANA
7056 
RURAL
1559 
SIN ESPECIFICAR
 
1

Length

Max length15
Median length6
Mean length5.8201021
Min length5

Characters and Unicode

Total characters50146
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowURBANA
2nd rowURBANA
3rd rowURBANA
4th rowURBANA
5th rowURBANA

Common Values

ValueCountFrequency (%)
URBANA 7056
81.9%
RURAL 1559
 
18.1%
SIN ESPECIFICAR 1
 
< 0.1%

Length

2023-08-10T22:48:31.909962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-10T22:48:32.010736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
urbana 7056
81.9%
rural 1559
 
18.1%
sin 1
 
< 0.1%
especificar 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 15672
31.3%
R 10175
20.3%
U 8615
17.2%
N 7057
14.1%
B 7056
14.1%
L 1559
 
3.1%
I 3
 
< 0.1%
S 2
 
< 0.1%
E 2
 
< 0.1%
C 2
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 50145
> 99.9%
Space Separator 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15672
31.3%
R 10175
20.3%
U 8615
17.2%
N 7057
14.1%
B 7056
14.1%
L 1559
 
3.1%
I 3
 
< 0.1%
S 2
 
< 0.1%
E 2
 
< 0.1%
C 2
 
< 0.1%
Other values (2) 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50145
> 99.9%
Common 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15672
31.3%
R 10175
20.3%
U 8615
17.2%
N 7057
14.1%
B 7056
14.1%
L 1559
 
3.1%
I 3
 
< 0.1%
S 2
 
< 0.1%
E 2
 
< 0.1%
C 2
 
< 0.1%
Other values (2) 2
 
< 0.1%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 15672
31.3%
R 10175
20.3%
U 8615
17.2%
N 7057
14.1%
B 7056
14.1%
L 1559
 
3.1%
I 3
 
< 0.1%
S 2
 
< 0.1%
E 2
 
< 0.1%
C 2
 
< 0.1%
Other values (3) 3
 
< 0.1%

STATUS
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.4 KiB
ABIERTA
6179 
CERRADA TEMPORALMENTE
2318 
TEMPORAL TITULOS
 
116
TEMPORAL NOMBRAMIENTO
 
3

Length

Max length21
Median length7
Mean length10.892526
Min length7

Characters and Unicode

Total characters93850
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowABIERTA
2nd rowABIERTA
3rd rowABIERTA
4th rowABIERTA
5th rowABIERTA

Common Values

ValueCountFrequency (%)
ABIERTA 6179
71.7%
CERRADA TEMPORALMENTE 2318
 
26.9%
TEMPORAL TITULOS 116
 
1.3%
TEMPORAL NOMBRAMIENTO 3
 
< 0.1%

Length

2023-08-10T22:48:32.114835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-10T22:48:32.251088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
abierta 6179
55.9%
cerrada 2318
 
21.0%
temporalmente 2318
 
21.0%
temporal 119
 
1.1%
titulos 116
 
1.0%
nombramiento 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 19434
20.7%
E 15573
16.6%
R 13255
14.1%
T 11169
11.9%
I 6298
 
6.7%
B 6182
 
6.6%
M 4761
 
5.1%
O 2559
 
2.7%
L 2553
 
2.7%
2437
 
2.6%
Other values (6) 9629
10.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 91413
97.4%
Space Separator 2437
 
2.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 19434
21.3%
E 15573
17.0%
R 13255
14.5%
T 11169
12.2%
I 6298
 
6.9%
B 6182
 
6.8%
M 4761
 
5.2%
O 2559
 
2.8%
L 2553
 
2.8%
P 2437
 
2.7%
Other values (5) 7192
 
7.9%
Space Separator
ValueCountFrequency (%)
2437
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 91413
97.4%
Common 2437
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 19434
21.3%
E 15573
17.0%
R 13255
14.5%
T 11169
12.2%
I 6298
 
6.9%
B 6182
 
6.8%
M 4761
 
5.2%
O 2559
 
2.8%
L 2553
 
2.8%
P 2437
 
2.7%
Other values (5) 7192
 
7.9%
Common
ValueCountFrequency (%)
2437
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 19434
20.7%
E 15573
16.6%
R 13255
14.1%
T 11169
11.9%
I 6298
 
6.7%
B 6182
 
6.6%
M 4761
 
5.1%
O 2559
 
2.7%
L 2553
 
2.7%
2437
 
2.6%
Other values (6) 9629
10.3%

MODALIDAD
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.4 KiB
MONOLINGUE
8362 
BILINGUE
 
254

Length

Max length10
Median length10
Mean length9.9410399
Min length8

Characters and Unicode

Total characters85652
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMONOLINGUE
2nd rowMONOLINGUE
3rd rowMONOLINGUE
4th rowMONOLINGUE
5th rowBILINGUE

Common Values

ValueCountFrequency (%)
MONOLINGUE 8362
97.1%
BILINGUE 254
 
2.9%

Length

2023-08-10T22:48:32.467916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-10T22:48:32.586923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
monolingue 8362
97.1%
bilingue 254
 
2.9%

Most occurring characters

ValueCountFrequency (%)
N 16978
19.8%
O 16724
19.5%
I 8870
10.4%
L 8616
10.1%
G 8616
10.1%
U 8616
10.1%
E 8616
10.1%
M 8362
9.8%
B 254
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 85652
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 16978
19.8%
O 16724
19.5%
I 8870
10.4%
L 8616
10.1%
G 8616
10.1%
U 8616
10.1%
E 8616
10.1%
M 8362
9.8%
B 254
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 85652
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 16978
19.8%
O 16724
19.5%
I 8870
10.4%
L 8616
10.1%
G 8616
10.1%
U 8616
10.1%
E 8616
10.1%
M 8362
9.8%
B 254
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85652
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 16978
19.8%
O 16724
19.5%
I 8870
10.4%
L 8616
10.1%
G 8616
10.1%
U 8616
10.1%
E 8616
10.1%
M 8362
9.8%
B 254
 
0.3%

JORNADA
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.4 KiB
DOBLE
2866 
VESPERTINA
2328 
MATUTINA
2221 
SIN JORNADA
836 
NOCTURNA
 
279

Length

Max length11
Median length10
Mean length7.8535283
Min length5

Characters and Unicode

Total characters67666
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMATUTINA
2nd rowMATUTINA
3rd rowMATUTINA
4th rowMATUTINA
5th rowVESPERTINA

Common Values

ValueCountFrequency (%)
DOBLE 2866
33.3%
VESPERTINA 2328
27.0%
MATUTINA 2221
25.8%
SIN JORNADA 836
 
9.7%
NOCTURNA 279
 
3.2%
INTERMEDIA 86
 
1.0%

Length

2023-08-10T22:48:32.675505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-10T22:48:32.819236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
doble 2866
30.3%
vespertina 2328
24.6%
matutina 2221
23.5%
sin 836
 
8.8%
jornada 836
 
8.8%
nocturna 279
 
3.0%
intermedia 86
 
0.9%

Most occurring characters

ValueCountFrequency (%)
A 8807
13.0%
E 7694
11.4%
T 7135
10.5%
N 6865
10.1%
I 5557
 
8.2%
O 3981
 
5.9%
D 3788
 
5.6%
R 3529
 
5.2%
S 3164
 
4.7%
L 2866
 
4.2%
Other values (8) 14280
21.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 66830
98.8%
Space Separator 836
 
1.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 8807
13.2%
E 7694
11.5%
T 7135
10.7%
N 6865
10.3%
I 5557
8.3%
O 3981
 
6.0%
D 3788
 
5.7%
R 3529
 
5.3%
S 3164
 
4.7%
L 2866
 
4.3%
Other values (7) 13444
20.1%
Space Separator
ValueCountFrequency (%)
836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 66830
98.8%
Common 836
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 8807
13.2%
E 7694
11.5%
T 7135
10.7%
N 6865
10.3%
I 5557
8.3%
O 3981
 
6.0%
D 3788
 
5.7%
R 3529
 
5.3%
S 3164
 
4.7%
L 2866
 
4.3%
Other values (7) 13444
20.1%
Common
ValueCountFrequency (%)
836
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 8807
13.0%
E 7694
11.4%
T 7135
10.5%
N 6865
10.1%
I 5557
 
8.2%
O 3981
 
5.9%
D 3788
 
5.6%
R 3529
 
5.2%
S 3164
 
4.7%
L 2866
 
4.2%
Other values (8) 14280
21.1%

PLAN
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size67.4 KiB
DIARIO(REGULAR)
5239 
FIN DE SEMANA
2254 
SEMIPRESENCIAL (FIN DE SEMANA)
 
424
SEMIPRESENCIAL (UN DIA A LA SEMANA)
 
337
A DISTANCIA
 
133
Other values (8)
 
229

Length

Max length37
Median length15
Mean length16.030989
Min length5

Characters and Unicode

Total characters138123
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDIARIO(REGULAR)
2nd rowDIARIO(REGULAR)
3rd rowDIARIO(REGULAR)
4th rowDIARIO(REGULAR)
5th rowDIARIO(REGULAR)

Common Values

ValueCountFrequency (%)
DIARIO(REGULAR) 5239
60.8%
FIN DE SEMANA 2254
26.2%
SEMIPRESENCIAL (FIN DE SEMANA) 424
 
4.9%
SEMIPRESENCIAL (UN DIA A LA SEMANA) 337
 
3.9%
A DISTANCIA 133
 
1.5%
SEMIPRESENCIAL 75
 
0.9%
SEMIPRESENCIAL (DOS DIAS A LA SEMANA) 52
 
0.6%
VIRTUAL A DISTANCIA 41
 
0.5%
SABATINO 40
 
0.5%
DOMINICAL 15
 
0.2%
Other values (3) 6
 
0.1%

Length

2023-08-10T22:48:32.971370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
diario(regular 5239
31.6%
semana 3067
18.5%
fin 2678
16.2%
de 2678
16.2%
semipresencial 888
 
5.4%
a 563
 
3.4%
la 389
 
2.3%
un 337
 
2.0%
dia 337
 
2.0%
distancia 174
 
1.1%
Other values (8) 206
 
1.2%

Most occurring characters

ValueCountFrequency (%)
A 19331
14.0%
R 16654
12.1%
I 15786
11.4%
E 13652
9.9%
D 8549
 
6.2%
7940
 
5.7%
N 7201
 
5.2%
L 6576
 
4.8%
( 6052
 
4.4%
) 6052
 
4.4%
Other values (12) 30330
22.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 118079
85.5%
Space Separator 7940
 
5.7%
Open Punctuation 6052
 
4.4%
Close Punctuation 6052
 
4.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 19331
16.4%
R 16654
14.1%
I 15786
13.4%
E 13652
11.6%
D 8549
7.2%
N 7201
 
6.1%
L 6576
 
5.6%
U 5619
 
4.8%
O 5350
 
4.5%
G 5241
 
4.4%
Other values (9) 14120
12.0%
Space Separator
ValueCountFrequency (%)
7940
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6052
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6052
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 118079
85.5%
Common 20044
 
14.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 19331
16.4%
R 16654
14.1%
I 15786
13.4%
E 13652
11.6%
D 8549
7.2%
N 7201
 
6.1%
L 6576
 
5.6%
U 5619
 
4.8%
O 5350
 
4.5%
G 5241
 
4.4%
Other values (9) 14120
12.0%
Common
ValueCountFrequency (%)
7940
39.6%
( 6052
30.2%
) 6052
30.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 138123
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 19331
14.0%
R 16654
12.1%
I 15786
11.4%
E 13652
9.9%
D 8549
 
6.2%
7940
 
5.7%
N 7201
 
5.2%
L 6576
 
4.8%
( 6052
 
4.4%
) 6052
 
4.4%
Other values (12) 30330
22.0%

DEPARTAMENTAL
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size67.4 KiB
GUATEMALA NORTE
1037 
GUATEMALA SUR
796 
GUATEMALA OCCIDENTE
774 
ESCUINTLA
599 
HUEHUETENANGO
495 
Other values (19)
4915 

Length

Max length19
Median length14
Mean length12.072772
Min length5

Characters and Unicode

Total characters104019
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowALTA VERAPAZ
2nd rowALTA VERAPAZ
3rd rowALTA VERAPAZ
4th rowALTA VERAPAZ
5th rowALTA VERAPAZ

Common Values

ValueCountFrequency (%)
GUATEMALA NORTE 1037
 
12.0%
GUATEMALA SUR 796
 
9.2%
GUATEMALA OCCIDENTE 774
 
9.0%
ESCUINTLA 599
 
7.0%
HUEHUETENANGO 495
 
5.7%
QUETZALTENANGO 476
 
5.5%
PETEN 379
 
4.4%
SUCHITEPEQUEZ 377
 
4.4%
GUATEMALA ORIENTE 363
 
4.2%
IZABAL 360
 
4.2%
Other values (14) 2960
34.4%

Length

2023-08-10T22:48:33.078181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
guatemala 2970
23.7%
norte 1084
 
8.6%
sur 796
 
6.3%
occidente 774
 
6.2%
escuintla 599
 
4.8%
huehuetenango 495
 
3.9%
quetzaltenango 476
 
3.8%
verapaz 468
 
3.7%
peten 379
 
3.0%
suchitepequez 377
 
3.0%
Other values (17) 4137
33.0%

Most occurring characters

ValueCountFrequency (%)
A 17565
16.9%
E 14108
13.6%
T 9811
 
9.4%
U 8423
 
8.1%
N 6351
 
6.1%
L 6167
 
5.9%
G 4412
 
4.2%
O 4297
 
4.1%
C 4114
 
4.0%
3939
 
3.8%
Other values (12) 24832
23.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 100080
96.2%
Space Separator 3939
 
3.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 17565
17.6%
E 14108
14.1%
T 9811
9.8%
U 8423
8.4%
N 6351
 
6.3%
L 6167
 
6.2%
G 4412
 
4.4%
O 4297
 
4.3%
C 4114
 
4.1%
M 3823
 
3.8%
Other values (11) 21009
21.0%
Space Separator
ValueCountFrequency (%)
3939
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 100080
96.2%
Common 3939
 
3.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 17565
17.6%
E 14108
14.1%
T 9811
9.8%
U 8423
8.4%
N 6351
 
6.3%
L 6167
 
6.2%
G 4412
 
4.4%
O 4297
 
4.3%
C 4114
 
4.1%
M 3823
 
3.8%
Other values (11) 21009
21.0%
Common
ValueCountFrequency (%)
3939
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104019
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 17565
16.9%
E 14108
13.6%
T 9811
 
9.4%
U 8423
 
8.1%
N 6351
 
6.1%
L 6167
 
5.9%
G 4412
 
4.2%
O 4297
 
4.1%
C 4114
 
4.0%
3939
 
3.8%
Other values (12) 24832
23.9%

CODIGO
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8616
Missing (%)100.0%
Memory size67.4 KiB

ZONA
Categorical

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)1.4%
Missing7080
Missing (%)82.2%
Memory size67.4 KiB
ZONA 1
628 
ZONA 7
173 
ZONA 12
114 
ZONA 18
102 
ZONA 6
71 
Other values (16)
448 

Length

Max length7
Median length6
Mean length6.3255208
Min length6

Characters and Unicode

Total characters9716
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZONA 1
2nd rowZONA 1
3rd rowZONA 1
4th rowZONA 1
5th rowZONA 1

Common Values

ValueCountFrequency (%)
ZONA 1 628
 
7.3%
ZONA 7 173
 
2.0%
ZONA 12 114
 
1.3%
ZONA 18 102
 
1.2%
ZONA 6 71
 
0.8%
ZONA 11 62
 
0.7%
ZONA 2 54
 
0.6%
ZONA 19 53
 
0.6%
ZONA 13 46
 
0.5%
ZONA 3 40
 
0.5%
Other values (11) 193
 
2.2%
(Missing) 7080
82.2%

Length

2023-08-10T22:48:33.178535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
zona 1536
50.0%
1 628
20.4%
7 173
 
5.6%
12 114
 
3.7%
18 102
 
3.3%
6 71
 
2.3%
11 62
 
2.0%
2 54
 
1.8%
19 53
 
1.7%
13 46
 
1.5%
Other values (12) 233
 
7.6%

Most occurring characters

ValueCountFrequency (%)
Z 1536
15.8%
O 1536
15.8%
N 1536
15.8%
A 1536
15.8%
1536
15.8%
1 1188
12.2%
2 202
 
2.1%
7 193
 
2.0%
8 107
 
1.1%
6 89
 
0.9%
Other values (5) 257
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6144
63.2%
Decimal Number 2036
 
21.0%
Space Separator 1536
 
15.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1188
58.3%
2 202
 
9.9%
7 193
 
9.5%
8 107
 
5.3%
6 89
 
4.4%
3 86
 
4.2%
9 81
 
4.0%
5 44
 
2.2%
0 27
 
1.3%
4 19
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
Z 1536
25.0%
O 1536
25.0%
N 1536
25.0%
A 1536
25.0%
Space Separator
ValueCountFrequency (%)
1536
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6144
63.2%
Common 3572
36.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1536
43.0%
1 1188
33.3%
2 202
 
5.7%
7 193
 
5.4%
8 107
 
3.0%
6 89
 
2.5%
3 86
 
2.4%
9 81
 
2.3%
5 44
 
1.2%
0 27
 
0.8%
Latin
ValueCountFrequency (%)
Z 1536
25.0%
O 1536
25.0%
N 1536
25.0%
A 1536
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Z 1536
15.8%
O 1536
15.8%
N 1536
15.8%
A 1536
15.8%
1536
15.8%
1 1188
12.2%
2 202
 
2.1%
7 193
 
2.0%
8 107
 
1.1%
6 89
 
0.9%
Other values (5) 257
 
2.6%

Interactions

2023-08-10T22:48:22.883190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-08-10T22:48:33.269591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Unnamed: 0DEPARTAMENTOSECTORAREASTATUSMODALIDADJORNADAPLANDEPARTAMENTALZONA
Unnamed: 01.0000.7900.1170.1730.1050.1520.1170.0820.8430.925
DEPARTAMENTO0.7901.0000.1620.2070.1240.3000.1300.1131.0001.000
SECTOR0.1170.1621.0000.1400.0780.1410.1500.1400.1660.225
AREA0.1730.2070.1401.0000.0350.1200.0900.0640.2210.265
STATUS0.1050.1240.0780.0351.0000.0270.1610.1340.1310.126
MODALIDAD0.1520.3000.1410.1200.0271.0000.0950.0820.3040.000
JORNADA0.1170.1300.1500.0900.1610.0951.0000.5610.1370.093
PLAN0.0820.1130.1400.0640.1340.0820.5611.0000.1200.065
DEPARTAMENTAL0.8431.0000.1660.2210.1310.3040.1370.1201.0000.994
ZONA0.9251.0000.2250.2650.1260.0000.0930.0650.9941.000

Missing values

2023-08-10T22:48:23.179095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-10T22:48:23.729939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-10T22:48:24.243774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0CODIGODISTRITODEPARTAMENTOMUNICIPIOESTABLECIMIENTODIRECCIONTELEFONOSUPERVISORDIRECTORNIVELSECTORAREASTATUSMODALIDADJORNADAPLANDEPARTAMENTALCODIGOZONA
0016-01-0138-4616-031ALTA VERAPAZCOBANCOLEGIO COBANKM.2 SALIDA A SAN JUAN CHAMELCO ZONA 877945104MERCEDES JOSEFINA TORRES GALVEZJULIO CESAR VILLELA AMADODIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEMATUTINADIARIO(REGULAR)ALTA VERAPAZNaNNaN
1116-01-0139-4616-031ALTA VERAPAZCOBANCOLEGIO PARTICULAR MIXTO VERAPAZKM 209.5 ENTRADA A LA CIUDAD77367402MERCEDES JOSEFINA TORRES GALVEZNaNDIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEMATUTINADIARIO(REGULAR)ALTA VERAPAZNaNNaN
2216-01-0140-4616-031ALTA VERAPAZCOBANCOLEGIO "LA INMACULADA"7A. AVENIDA 11-109 ZONA 678232301MERCEDES JOSEFINA TORRES GALVEZVIRGINA SOLANO SERRANODIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEMATUTINADIARIO(REGULAR)ALTA VERAPAZNaNNaN
3316-01-0141-4616-005ALTA VERAPAZCOBANESCUELA NACIONAL DE CIENCIAS COMERCIALES2A CALLE 11-10 ZONA 279514215RUDY ADOLFO TOT OCHNaNDIVERSIFICADOOFICIALURBANAABIERTAMONOLINGUEMATUTINADIARIO(REGULAR)ALTA VERAPAZNaNNaN
4416-01-0142-4616-005ALTA VERAPAZCOBANINSTITUTO NORMAL MIXTO DEL NORTE 'EMILIO ROSALES PONCE'3A AVE 6-23 ZONA 1179521468RUDY ADOLFO TOT OCHNaNDIVERSIFICADOOFICIALURBANAABIERTABILINGUEVESPERTINADIARIO(REGULAR)ALTA VERAPAZNaNNaN
5516-01-0143-4616-031ALTA VERAPAZCOBANCOLEGIO PARTICULAR MIXTO IMPERIAL5A. CALLE 1-9 ZONA 357101061MERCEDES JOSEFINA TORRES GALVEZHECOTR WALDEMAR TOT COYDIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEDOBLEFIN DE SEMANAALTA VERAPAZNaNNaN
6616-01-0145-4616-006ALTA VERAPAZCOBANINSTITUTO DE TURSMO Y AVIACON DEL NORTE I.T.A.N3 AV. 5-28 ZONA 454641454EFRAIN CAAL CUCLUIS FERNANDO SOTODIVERSIFICADOPRIVADOURBANACERRADA TEMPORALMENTEMONOLINGUEMATUTINADIARIO(REGULAR)ALTA VERAPAZNaNNaN
7716-01-0147-4616-031ALTA VERAPAZCOBANCOLEGIO "LA INMACULADA"7A. CALLE 11-09 ZONA 6 COBAN49532425MERCEDES JOSEFINA TORRES GALVEZMERCEDES QUIROS QUIROSDIVERSIFICADOPRIVADORURALCERRADA TEMPORALMENTEMONOLINGUEDOBLEDIARIO(REGULAR)ALTA VERAPAZNaNNaN
8816-01-0150-4616-006ALTA VERAPAZCOBANINSTITUTO INTERCULTRUAL ALTAVERAPACENCESE -IIAV-3A. AVAENIDA 1-23 ZONA 4NaNEFRAIN CAAL CUCGUILLERMO ESTUARDO VASQUEZ MORALESDIVERSIFICADOPRIVADOURBANACERRADA TEMPORALMENTEBILINGUEDOBLEFIN DE SEMANAALTA VERAPAZNaNNaN
9916-01-0155-4616-031ALTA VERAPAZCOBANLICEO "MODERNO LATINO"11 AVENIDA 5-17 ZONA 479522555MERCEDES JOSEFINA TORRES GALVEZJORGE BENEDICTO COC POPDIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEDOBLEFIN DE SEMANAALTA VERAPAZNaNNaN
Unnamed: 0CODIGODISTRITODEPARTAMENTOMUNICIPIOESTABLECIMIENTODIRECCIONTELEFONOSUPERVISORDIRECTORNIVELSECTORAREASTATUSMODALIDADJORNADAPLANDEPARTAMENTALCODIGOZONA
8606900612-29-4728-4612-053SAN MARCOSSAN LORENZOINSTITUTO DIVERSIFICADO 'DR. JUAN JOSE AREVALO BERMEJO'SAN LORENZONaNAMILCAR ROCAEL VELASQUEZ OROZCONaNDIVERSIFICADOPRIVADOURBANACERRADA TEMPORALMENTEMONOLINGUEVESPERTINADIARIO(REGULAR)SAN MARCOSNaNNaN
8607900712-29-5026-4612-053SAN MARCOSSAN LORENZOINSTITUTO TECNOLOGICO PRIVADO MIXTO EVANGELICO SUNESISSAN LORENZO51786859AMILCAR ROCAEL VELASQUEZ OROZCOEMIR OSBELI FUENTES VASQUEZDIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEMATUTINADIARIO(REGULAR)SAN MARCOSNaNNaN
8608900812-30-0046-4612-107SAN MARCOSLA BLANCAINSTITUTO NACIONAL DE EDUCACION DIVERSIFICADACABECERA MUNICIPAL41016246JUAN JOSE TOBAR TEBALANWALTER RENE PEREZ Y PEREZDIVERSIFICADOOFICIALURBANAABIERTAMONOLINGUEVESPERTINADIARIO(REGULAR)SAN MARCOSNaNNaN
8609900912-30-0051-4612-107SAN MARCOSLA BLANCACOLEGIO ADVENTISTA MARANATHACABECERA MUNICIPAL49582374JUAN JOSE TOBAR TEBALANSONIA NOEMI GARCIA FUENTES DE AGUIRREDIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEVESPERTINADIARIO(REGULAR)SAN MARCOSNaNNaN
8610901012-30-0052-4612-107SAN MARCOSLA BLANCACOLEGIO ADVENTISTA MARANATHACABECERA MUNICIPAL49582374JUAN JOSE TOBAR TEBALANSONIA NOEMI GARCIA FUENTES DE AGUIRREDIVERSIFICADOPRIVADOURBANACERRADA TEMPORALMENTEMONOLINGUEDOBLEDOMINICALSAN MARCOSNaNNaN
8611901112-30-0056-4612-107SAN MARCOSLA BLANCACOLEGIO PRIVADO URBANO MIXTO LICEO MODERNOCABECERA MUNICIPAL58899435JUAN JOSE TOBAR TEBALANMELVI WALDIR HURTADO CIFUENTESDIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEVESPERTINADIARIO(REGULAR)SAN MARCOSNaNNaN
8612901212-30-0057-4612-107SAN MARCOSLA BLANCACOLEGIO PRIVADO URBANO MIXTO LICEO MODERNOCABECERA MUNICIPAL58899435JUAN JOSE TOBAR TEBALANMELVI WALDIR HURTADO CIFUENTESDIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEDOBLEFIN DE SEMANASAN MARCOSNaNNaN
8613902012-30-0085-4612-093SAN MARCOSLA BLANCACOLEGIO ADVENTISTA MARANATHACABECERA MUNICIPAL49582374JUAN JOSE TOBAR TEBALANUSIELA SARONITA CARRETO LOPEZDIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUESIN JORNADASEMIPRESENCIAL (UN DIA A LA SEMANA)SAN MARCOSNaNNaN
8614902112-30-0087-4612-107SAN MARCOSLA BLANCACOLEGIO EDUCATIVO MIXTO LUNA AZUL -CEMLA-PARCELAMIENTO CHIQUIRINES45454204JUAN JOSE TOBAR TEBALANBETY PATRICIA OXLAJ PEREZDIVERSIFICADOPRIVADORURALCERRADA TEMPORALMENTEMONOLINGUESIN JORNADASEMIPRESENCIAL (UN DIA A LA SEMANA)SAN MARCOSNaNNaN
8615902212-30-0089-4612-107SAN MARCOSLA BLANCACOLEGIO PRIVADO URBANO MIXTO LICEO MODERNOCABECERA MUNICIPAL36121181JUAN JOSE TOBAR TEBALANMAVERIK GEYSTYNG HURTADO CIFUENTESDIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUESIN JORNADASEMIPRESENCIAL (UN DIA A LA SEMANA)SAN MARCOSNaNNaN